Gait analysis system and methods

ABSTRACT

Systems and methods for analyzing the gait of an individual are disclosed. The disclosed systems and methods can be configured to acquire data from a first array and a second array of sensors that are configured to be placed in a left and/or right shoe, respectively. The acquired data can be collected or separated into at least two separate gait phases for each array, compared to a baseline condition for each gait phase and categorized into one of at least two uniformity categories for each gait phase. Examples of collected and/or calculated data include pressure values, shear stress values and torque values. The analysis can be focused on both feet of a person, or focused on one foot. A graphical output showing at least one entire gait cycle based on the uniformity categories can then be generated.

CROSS REFERENCE TO RELATED APPLICATION

This application is a divisional of application Ser. No. 14/097,903,filed Dec. 5, 2013, which is a divisional of application Ser. No.12/851,614, filed Aug. 6, 2010, now U.S. Pat. No. 8,628,485, whichapplications are incorporated herein by reference in their entirety.

TECHNICAL FIELD

This application relates to a system and methods for collecting,calculating and outputting data useful in analyzing the gait of anindividual.

BACKGROUND

Disorders of asymmetries and/or imbalances in gait have been associatedwith significant clinical morbidity, mortality, and healthcare cost andresource utilization. For example, loss of balance and falls can resultin acute injuries, hospitalization, and deaths. Additionally, theprogressive deterioration of the joints, either with associated pain orwithout pain, can cause balance/gait disorders. For example, injuries tothe anterior cruciate ligament can lead to deterioration of the kneejoint anatomy and function. Another example is the deterioration at theknee or hip joint anatomy and function secondary to rheumatoid arthritisand/or osteoarthritis, either before or after partial or total hip jointreplacement surgery. Yet another cause of balance/gait disorders isunequal weight bearing between the lower extremities, resulting inchronic musculoskeletal pain, including back pain. As might beappreciated, numerous challenges exist in preventing, treating andrehabilitating balance and gait disorders.

Even though the causes for many balance and gait disorders are wellunderstood, improvements in assessment tools for analyzing thesedisorders are desired. This is particularly the case where it is desiredto assess gait and/or balance quantitatively during the totality ofambulation and activity over a prolonged period, for example, over thecourse of a full day.

SUMMARY

Systems and methods for analyzing the gait of an individual aredisclosed. In one method, data are acquired from a first array and asecond array of pressure sensors and/or shear stress sensors that areconfigured to be placed in a left and right shoe, respectively. By theuse of the term “shoe” it is broadly intended to mean any foot appliancesuitable for fitting sensors that will be on or near an individual'sfoot. By way of non-limiting examples, a shoe can be a walking shoe, adress shoe, a running shoe, a sandal, a slipper, or a foot appliancedesigned for the specific purpose of assessing a person's gait. Theacquired data are collected or separated into at least two separate gaitphases for each array and then compared to a baseline condition for eachgait phase. The pressure sensors in the array are then categorized intoone of at least two pressure uniformity categories for each gait phasebased on the results of the comparison of the acquired data to thebaseline condition. A graphical output showing at least one entire gaitcycle based on the pressure uniformity categories can then be generated.An additional graphical output showing shear stress and resultant torquevalues can be overlaid onto the graphical output showing the pressureuniformity categories. It should also be noted that the system can beused to evaluate a single foot of an individual, or can be used toevaluate both feet.

In another method, data are acquired onto a computerized storage device,transformed into a data evaluation set and analyzed. The acquired datacan comprise pressure and time information from a first array ofpressure sensors disposed in a left shoe and a second array of pressuresensors disposed in a right shoe wherein each pressure sensor in thefirst array having a corresponding and similarly located pressure sensorin the second array that together form a pressure sensor pair. The dataevaluation set can be created by parsing at least some of the acquireddata into at least two separate gait phases for each array, calculatinga mean pressure value for each sensor for each similar gait phase, andcalculating a mean pressure value for each sensor pair for each similargait phase. The data can be analyzed by comparing, for each gait phase,the mean pressure value for each sensor to the sensor pair mean pressurevalue and to a mean pressure deviation limit value, and categorizingeach sensor into one of at least two pressure uniformity categories foreach gait phase on the basis of the comparison. Instead of, or inaddition to, using mean data, median data may also be used. The methodcan also comprise creating a graphical output based on the category intowhich each sensor has been placed wherein the output shows at least oneentire gait cycle wherein each gait phase is individually represented bya right footprint and/or a left footprint. Additionally, the graphicaloutput can show shaded, patterned or colored areas correlating to thepressure uniformity category for each pressure sensor on each footprintfor each gait phase in the gait cycle wherein the shaded, patterned orcolored areas are shown on each footprint at a location corresponding tothe actual sensor location within the shoe. Examples of patternsinclude, among many others, hatching and repeated use of pre-definedsymbols or shapes.

In one exemplary system, a first array of pressure and/or shear stresssensors is configured to be positioned in a left shoe and a first datatransmitter is configured to transmit stress, pressure and time datafrom the first array of pressure and/or shear stress sensors to a datacollection device. A second array of pressure and/or shear stresssensors is also configured to be positioned in a right shoe and a seconddata transmitter is configured to transmit stress, pressure and timedata from the second array of pressure and/or shear stress sensors to adata collection device. A data collection device can also be part of thesystem to receive data from the first and second transmitters.

The system can also include a computer processor constructed andconfigured to: compare, for at least two separate gait phases, at leasta portion of the acquired data to a baseline condition; and tocategorize the pressure sensors in each array, or a group of pressuresensors in each array, into one of at least two pressure uniformitycategories for each gait phase based on the comparison of the acquireddata to the baseline condition. The computer processor can also beconstructed and configured to: calculate a mean and/or median pressurevalue for each sensor for each similar gait phase; calculate a meanand/or median pressure value for each sensor pair for each similar gaitphase; compare, for each gait phase, the mean and/or median pressurevalue for each sensor to the sensor pair mean pressure value and to amean and/or median pressure deviation limit value; and categorize eachsensor into one of at least two pressure uniformity categories for eachgait phase on the basis of the comparison of the sensor to the sensorpair.

The disclosed gait analysis system is specifically at least able torecord the following parameters while the individual is ambulatory andis engaged in activities of daily living either indoors or outdoorswhile at home, work and recreational environments: pressures and shearstresses recorded at each individual sensor recorded over time; pressureconfigurations that may be analyzed as pressure magnitude maps and/or asbest-approximation polygons of three or more sides; pressuredistribution by area during components of gait cycles; pressureconfiguration patterns and timing from or between components of gaitcycles, including heel strike, mid-stance and toe-off either for asingle lower extremity or for comparisons between both lowerextremities; total weight-bearing magnitude forces during components ofa gait cycle; and the relationship of the above parameters over timewithin a given gait cycle, e.g., heel strike, mid-stance and toe-off.The analysis of the above mentioned parameters can allow a health careprofessional to determine if the center of pressure, the shear stressand the resulting torque at the sole is consistent with, as an example,symmetrical weight bearing at the knee joint of the same lowerextremity. The data from the soles of both lower extremities can also beanalyzed together to determine and to compare the centers of pressure toevaluate if, as an example, symmetrical weight bearing is occurring atthe knees and at the hips of both lower extremities and to determine thecenter of pressure and the pattern of weight bearing is distributedsymmetrically to the spine.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a schematic view of a first embodiment of a gait analysissystem.

FIG. 2a is a diagrammatic top view of an arrangement of pressure sensorsfor a left shoe and a right shoe.

FIG. 2b is a diagrammatic top view of an arrangement of pressure sensorsand shear stress sensors for a left shoe and a right shoe.

FIG. 3 is a representation of three gait phases for a right foot duringa single gait cycle.

FIG. 4 is a first example of a graphical output from the gait analysissystem of FIG. 1.

FIG. 5 is a second example of a graphical output from the gait analysissystem of FIG. 1.

FIG. 6 is a third example of a graphical output from the gait analysissystem of FIG. 1.

FIG. 7 is a fourth example of a graphical output from the gait analysissystem of FIG. 1.

FIG. 8 is a fifth example of a graphical output from the gait analysissystem of FIG. 1.

FIG. 9 is a further representation of the fourth example shown in FIG.7.

FIG. 10 is a flow chart showing example steps for analyzing anindividual's gait.

FIG. 11 is a flow chart showing example steps for analyzing andcategorizing acquired data.

FIG. 12 is an example input table for the gait analysis system of FIG. 1

FIG. 13 is a first example output table from the gait analysis system ofFIG. 1.

FIGS. 14-16 show a second example output table from the gait analysissystem. of FIG. 1

DETAILED DESCRIPTION

This disclosure relates to a system and methods for analyzing the gaitof an individual. In broad terms, pressure data from a left shoe and aright shoe are acquired and evaluated to determine if an individual'sgait is in need of improvement. One example of such a system is gaitanalysis system 100, shown on FIG. 1.

In one exemplary embodiment, gait analysis system 100 includes a firstarray of sensors 112 disposed within a right shoe 110 and a second arrayof sensors 122 disposed within a left shoe 120. Many types of sensorsare useful in gait analysis system 100. For example, sensors 112 a, 122a can be pressure sensors. Alternatively, the sensors can be shearstress sensors 152 a, 162 a, for example biaxial shear stress sensors.By the use of the term “biaxial” it is meant that at least two componentvalues for shear stress are measured along different axes, preferablyorthogonal axes. In some arrangements, both biaxial shear stress sensorsand pressure sensors will be used in the same shoe.

In the particular embodiment shown in FIG. 2a , the first and secondarrays of sensors 112, 122 are for measuring the pressure that anindividual's foot exerts over an area of the foot. In the embodimentshown in FIG. 2b , the first and second arrays of sensors 112, 122 arefor measuring not only the pressure, but also the shear stress that anindividual's foot exerts over an area of the foot. Many types andconfigurations of pressure sensors and shear stress sensors, andcombinations thereof are suitable for this purpose. For example, FIG. 2ashows a plurality of pressure sensors 112 a, 122 a disposed in variouslocations to form a first and second array of pressure sensors 112, 122,respectively. FIG. 2b shows a plurality of pressure sensors 112 a, 122 aand a plurality of shear stress sensors 152 a, 162 a disposed in variouslocations to form a first and second array of pressure sensors 112, 122,respectively. As can be seen at FIGS. 2a and 2b , each array includesnumerous individual sensors arranged to cover the major contacting areasof a foot (only type of sensor in each array is actually labeled). Ofcourse, the arrays 112, 122 could be configured with fewer or moresensors, or in conjunction with a pressure sensing fabric.

In the actual arrangement shown in FIGS. 2a and 2b , each sensor 112 a,152 a in the first array 112 has a correspondingly located sensor 122 a,162 a in the second array 122 to form a sensor pair. As shown at FIGS.2a and 2b , the two pressure sensors actually labeled 112 a and 122 aform such a pair as do sensors 152 a and 162 a. Also, some of thesensors can be grouped together such that an output is generated basedon the average output for the grouped sensors. In this case, rather thanhaving sensor pairs, the sensors that are grouped together would formgroup sensor pairs 112 b, 122 b and/or 152 b, 162 b. Where a fabric isused, the same principle can be applied to pre-defined areas over thefabric. Additionally, it should be noted that the first and secondpressure arrays 112, 122 can be integral to a shoe, or can be arrangedon a removable insert. In the latter case, a potential benefit exists inthat the pressure arrays 112, 122 can be used in conjunction with anindividual's normally used shoes.

Gait analysis system 100 can also include transmitters 116, 126 forreceiving and transmitting output information from the first and secondpressure arrays 112, 122 to unit 130, discussed later. As shown in FIG.1, the transmitters 116, 126 are connected to the first and secondarrays 112, 122 via connections 114, 124, respectively. Connections 114,124 can be made via either cable(s) or a wireless connection. As shown,connections 114, 124 are cables that are directly connected to eachsensor 112 a, 122 a while connections 118 and 128 to unit 130 arewireless connections. However, one skilled in the art will appreciatethat other configurations are suitable. For example, some of the sensorscan be wired together or selectively grouped via software such that anoutput is generated based on the average pressure output for groupedsensors, 122 b.

As noted in the preceding paragraph, gait analysis system 100 can alsoinclude a unit 130 for receiving data from the transmitters 116, 126. Asshown, unit 130 is configured to acquire sensor output information, suchas time, stress and pressure values, from the first and second arrays112, 122 via transmitters 116, 126. Unit 130 can also be configured tostore the data from the transmitters and to perform calculationsrelating to the data. In either case, unit 130 can also be configured toupload and download information from a computer 140 via connection 132.As shown, connection 132 is a wireless connection, but a cableconnection is just as feasible. Additionally, computer 140 can also beconfigured with a display screen 142 to show graphical output generatedeither by unit 130 or computer 140. Non-limiting examples of informationthat could be uploaded to the computer 140 from unit 130 are rawpressure data from the pressure sensors (e.g. output voltage and time),raw data from the shear stress sensors, calculated results such as meanand/or median values for the pressure data over a period of time, nettorque exerted across a group of shear stress sensors, and graphicaloutput information. Non-limiting examples of information that could bedownloaded to unit 130 from computer 140 are configuration parameters,such as specific times to acquire output data, desired sensor groupings,and parameters for defining individual gait phases. It should also benoted that many of the above described functions for transmitters 116,126, unit 130 and computer 140 do not necessarily need to be performedby one device or the other. For example, the calculations necessary tocreate a graphical representation could be performed by the computer 140instead of unit 130.

Referring to FIGS. 4-8, exemplary graphical outputs are shown that canbe displayed on an electronic display, such as screen 142, and/orgenerated in hard copy form. In each of FIGS. 4-8, a gait pattern withfootprints is shown representing an intended direction of ambulation 214wherein each individual footprint represents a separate gait phase 202.The shown gait phases shown for each foot are heel strike 204,mid-stance 206, and toe-off 208. Each gait phase 202 corresponds to asegment of time during which the foot or shoe is in contact with theground such that the sum of all three gait phases represents the totalcontact time. To further illustrate this concept, these three gaitphases are also shown in FIG. 3 which depicts a side view of anindividual's right foot 210 and representative pressure values 212 asthe foot moves through each gait phase 204, 206, 208. Taken together,the gait phases for each foot form one entire gait cycle 200. A gaitcycle 200 is defined as all defined gait phases for sequentiallyadjacent left and right foot placements. In the particular embodimentsshown in FIGS. 4-8, gait cycle 200 is comprised of the following gaitphases 202 from the bottom of the page up: left heel strike 204, leftmid-stance 206, left toe-off 208, right heel strike 204, rightmid-stance 206, and right toe-off 208. It is, of course, possible toparse the contact time into fewer or more than the three gait phasesshown for each foot. It is also possible to show only the gait phasesfor a gait cycle 200 of only one foot where an analysis is not concernedwith both feet. In such a case the system would only require thecollection of data from sensors associated with the foot to be analyzed.

Still referring to FIGS. 4-8, shaded areas are shown for each footprintand gait phase to represent pressure values. With specific reference toFIG. 5, arrows are also shown to represent shear stress and torquevalues. These shaded areas and arrows are for showing whether anindividual's gait is in conformity with a baseline condition. Manyexamples exist for a baseline condition. In one example, the baselinecondition can be a model of calculated values for pressure, shearstress, and/or torque over time that represents a typical gait, or agait having no apparent abnormalities. In such an example, the model canbe based upon an individual of the same or similar physicalcharacteristics, including height, weight, gender, as the individualbeing analyzed. In this case, the shaded areas would represent thedifference between the actual gait of the individual and the modeledgait. Another example is where the baseline condition is derived fromvalues obtained from a previous gait/balance test or from an initialgait baseline test for the individual being analyzed. In this case, theshaded areas would represent any changes that have occurred since thebaseline test or the previous test. Yet another example is where thebaseline condition is actually the combined sensor data from the sensorpair associated with each sensor. Where this is the case, the shadedareas represent the degree to which one foot is exerting more or lesspressure, shear stress or torque than the mean value for both feet at aparticular location for each gait phase. Any of these baselineconditions is equally useful when looking at sensor data on anindividual output basis, or when looking at aggregated sensor data thathas been averaged together to create a mean value for each gait phase.When looking at individual output values, the comparison is useful toassess a specific event, such as the conditions that led up to anindividual's loss of balance and subsequent fall. When looking at meanand/or median values for each gait phase, the comparison is useful tolongitudinally assess an individual's typical gait throughout the courseof a day, which may vary significantly from a simple baseline test in alaboratory setting. It is noted that the graphical output can beconfigured to selectively show one or more of the shaded areas, theshear stress arrows, and the torque arrows such that only therepresentations relevant to the analysis are shown.

In the exemplary embodiments shown in FIGS. 4-8, the shaded areas areshown in three gray-scale tones: dark gray, medium gray, and light gray.These tones can represent different pressure uniformity categoriesrelating to the comparison of the pressure sensor data to the baselinecondition. For example, dark gray represents a “non-uniform high”category wherein the pressure sensor data are above the baselinecondition, medium gray represents a “uniform” category wherein thepressure sensor data are within the baseline condition, and light grayrepresents a “non-uniform low” category wherein the pressure sensor dataare below the baseline condition. The “uniform category” can correlateto a baseline condition that is defined by a range of acceptable values.This could be accomplished by selecting a high value and a low valuethat bound the baseline condition. Alternatively, the range could bedefined by a mean value and a mean deviation limit wherein the rangeextends from the mean value minus the mean deviation limit to the meanvalue plus the mean deviation limit. In a similar fashion, median valuescan also be used. In either case, the non-uniform high and non-uniformlow categories would correlate to values above or below the baselinecondition, respectively. With such an approach, a health careprofessional can simply vary the mean deviation limit for a particularindividual in order to account for the fact that some gait/balancedisorders require a more sensitive assessment than others. Additionally,although FIGS. 4-8 are shown using a three-tone grayscale representationthat relate to three pressure uniformity categories, one skilled in theart will appreciate that more tones/colors and categories may be used toshow a higher degree of resolution.

It is noted that the shaded areas shown are derived from data collectedfrom the pressure sensors and subsequent calculations. Although thesensors are located in discreet positions within the shoe, the graphicaloutput can be created such that a smooth, gradated pressure pattern isachieved, as shown in FIGS. 4-8. However, it is entirely possible toshow segmented regions on the graphical output such that the output foreach pressure sensor is more clearly identified. Showing an entire gaitcycle 200 with shaded areas in the manner described is beneficialbecause many gait and balance disorders can be more easily evaluatedwith such visual information.

With particular reference to FIGS. 4-5, the shaded areas for the gaitcycle shown represent output derived from a comparison of the pressuresensor values against a baseline condition where all of the pressuresensor values are within the baseline condition parameters for each gaitphase. This can be readily seen by the condition that all of the shadedareas in FIG. 4 are medium gray. Such output is the result of anindividual's gait that is either normal, consistent with a previous testand/or in a balanced state, depending upon the nature of the baselinecondition utilized.

Output from an individual with a balance disorder that can lead to afall is shown in FIG. 6. As shown in FIG. 6, the shaded areas indicatethat the individual is going from heel strike to mid-stance and thenback to heel strike. During this gait cycle, the individual stays inmid-stance and alternates pressure on the lateral aspect of the sole andthen to the medial aspect of the sole and then back again. By viewingthe entire gait cycle in this manner, a health care professional isbetter able to evaluate and understand the circumstances leading up toand surrounding a loss of balance event that may have led to a fall. Thebaseline condition for the output shown in FIG. 6 is a set of pressurevalues corresponding to a normal gait pattern.

Another example of a useful graphical output is shown at FIG. 7 wherelongitudinal pressure sensor data over a period of time has beencollected and averaged to create mean values for each gait phase. Here,an individual with pain or other dysfunction at any point in either orboth lower extremities may adopt a gain that imbalances weight-bearingand thereby transmits imbalanced musculoskeletal, mechanical forces tothe spine. These imbalanced forces may be associated with back pain. Theuse of the gait analysis system and the associate graphical output willhelp to assess the balance or lack thereof by allowing a quantitative,comparative analysis of the pressure patterns and proportions ofpressure being exerted on the two different lower extremities over time.The baseline comparison for the output shown in FIG. 7 is a set ofpressure values corresponding to a normal gait pattern.

Yet another example of how showing at least a single gait cycle isbeneficial is represented in FIG. 8 where longitudinal pressure sensordata over a period of time has been collected and averaged to createmean values for each gait phase. Here, an individual with osteoarthritisof the knee associated with pain in the lateral aspect of that knee mayadopt an abnormal and deleterious gait. Such a gait might relieve painin the short term and accelerate knee joint destruction more quicklyover time. The physiologic basis is to partially unload the painfullateral aspect of that same knee by modifying components of the gaitcycle to focus weight-bearing on the lateral aspects of the sole of thefoot of that extremity. By so doing, most of the weight-bearing isshifted to the medial aspect of that knee joint. Such a change in gaitcan be detected by measurement and analysis of the graphical outputshowing at least a single gait cycle. In order to prevent further ormore rapid deterioration of that knee joint, gait training, includingstrength training, may be instituted and then monitored by the gaitanalyses system over time. This condition is also partially representedat FIG. 9 where it is further shown how the center of pressure (COP) ofthe foot undergoes a lateral shift. The graphical output for any of thedepictions shown in FIGS. 4-8 can also show the COP of the foot, or avariance between the actual COP and a baseline condition COP. Thebaseline comparison for the output shown in FIG. 8 is a set of pressurevalues corresponding to a normal gait pattern.

Referring back to FIG. 5, additional graphical information is presentedregarding an individual's gait that is not shown in FIG. 4.Specifically, FIG. 5 shows shear stress direction and magnitude arrowsfor each gait phase in addition to applied torque for each gait phase.Such information is enabled by locating biaxial shear stress sensorswithin the shoes 110, 120. In the particular embodiment shown, the totalshear stress 224 is broken down into a longitudinal shear stress 220component and a lateral shear stress component 222. For the purpose ofclarity, these features are labeled on an enlarged footprint indicatedby dashed arrow A. The length, color and/or width of each of the shownarrows 220, 222, 224 can be related to the raw magnitude of the stressexperienced by the sensor. Alternatively, and as described previouslyfor the pressure values, the length and/or width of each arrow 220, 222,224 can reflect the result of a comparison of measured values to abaseline condition. For example, the measured shear stress values from abaseline test can be compared to those acquired during a subsequenttest. In this case, the magnitude of the shear stress arrows wouldreflect the difference between the baseline and subsequent test.Additionally, the direction of the shear stress arrows 220, 222, 224 canbe oriented to show the actual direction of the shear stress appliedalong the measured axis, or the net direction of shear stress whencompared to a baseline condition. It is noted that the direction andrelative magnitude of the arrows in FIG. 5 are schematic and notintended to represent an actual or expected output from the system. Inthe example shown in FIG. 5, the longitudinal shear stress arrow 220changes from a force applied in a direction extending from the healtowards the toes during the heal strike gait phase 204, to a net zeroforce during the mid-stance gait phase, to a force applied in adirection extending from the toes towards the heal during the toe offgait phase. Through the use of the shear stress sensors, it is alsopossible to calculate a torque value for each gait phase, and to show atorque value arrow 226. Similarly to the shear stress arrows, thedirection and length/width/color of the torque arrow 226 can changedepending on the magnitude and direction of the torque applied to theshoe. Furthermore, the torque arrow 226, and the shear stress arrows220, 222, 224 can be arranged about a central axis 228 that correspondsto the center of the applied torque. It is also noted that other methodsfor graphically depicting the magnitude and direction of the shearstress and torque values besides the use of arrows is possible withoutdeparting from the concepts presented herein. Furthermore, one skilledin the art will appreciate that values for these parameters can bepresented in tabular form wherein pressure, stress and torque can beshown in isolation or together for easier viewing and analysis.

Referring to FIG. 10, a flow chart is shown that demonstrates steps thatcan be used to arrive at the above described graphical output examplesby using the gait analysis system 100. In a first step, the gaitanalysis system 100 is activated. This step can include outfitting theindividual with the shoes 110, 120 and unit 130, and enabling unit 130to start recording data. After the gait analysis system 100 isactivated, unit 130 can acquire sensor and time data over either apredetermined or open ended period of time. If desired, the health careprofessional can guide the individual through a series of tests whichcan be used to establish an initial baseline. This initial baseline canbe used as the baseline condition, or can be compared to anotherbaseline condition for further analysis. The initial baseline caninclude activities such as walking, running, and walking up or downstairs. During the actual testing period, unit 130 will collect datauntil it is deactivated. After a desired period of time has passed, or adesired amount of data has been collected, unit 130 can be returned tothe health care professional for analysis of the data.

Another step is for the health care professional to define parametersfor segregating and parsing the acquired data. Alternatively, theparameters can be pre-configured in the system such that no input isneeded from the health care professional. This step can be performedbefore or after the previously described step of acquiring the sensordata. If this step is performed before the data acquisition step, unit130 can also be configured to acquire only sensor data that is withinthe specified parameters rather than simply collecting all data.Alternatively, a desired data subset can be extracted from the acquireddata set after data acquisition is complete. One example of a parameterfor segregating the data includes specifying that only data recordedduring ambulatory periods is collected and/or analyzed. This can beaccomplished in unit 130 by monitoring for gait cycles that occur, forexample within a predefined period of time. This can also beaccomplished through interaction with a user interface, such as abutton, that the individual can use to identify periods of ambulation.An example of a parameter for parsing the acquired data to be analyzedincludes defining how sensor data are grouped into individual gaitphases. This can be accomplished by defining gait phases as a percentageof the contact time. For example, the gait phases can be defined as: 1)heel strike phase being 0% to 15% of the total foot contact time withthe ground; 2) mid-stance phase being between 15% and 55% of the contacttime; and 3) toe off phase being 55% to 100% of the contact time. Oneskilled in the art will recognize that other methods for parsing theacquired data to be analyzed into separate gait phases are alsopossible. Another parameter for parsing the acquired data can be thespecification of how pressure sensor readings are grouped together.

Another step in the shown process is to define comparison parameters forevaluating the acquired data. The comparison parameters are for settingup an analysis that will allow a uniformity category to be assigned toeach pressure sensor, or sensor group, in the arrays. An example of aparameter for a comparison parameter is the definition of the baselinecondition. As stated previously, the baseline condition can beestablished through a mean value and a mean deviation or variationthreshold limit, or through the selection of a range of values.Alternatively, the baseline condition can be defined by a mean value, anupper deviation limit and a lower deviation limit. Furthermore, thebaseline condition can be established through the use of values derivedfrom a baseline test, a previous analysis, or a calculated normal gaitfor the individual. In the above examples, the baseline conditionenables the use of three possible outputs in that the acquired data willeither be above, below or within the baseline condition. It is alsonoted that multiple baseline conditions can be established through theuse of multiple deviation limits, multiple ranges, or by other methodsknown in the art. As stated previously, the relationship of the sensorvalues to the baseline condition provides the basis for categorizing thesensor or senor groups into uniformity categories.

After all of the parameters or all of the selected parameters ofinterest have been established, it is then possible for the gaitanalysis system to perform the necessary calculations on the acquireddata in order to assign an appropriate uniformity category. One exampleof how these calculations can be performed is shown in FIG. 11 wheregait analysis system 100 acquires data over a period of time which isthen parsed into separate gait phases based on the defined parameters.After this step, a mean value is calculated for each sensor for allreadings acquired during each gait phase. In this example, a mean valueis also calculated for each sensor pair for all readings acquired duringeach gait phase. The sensor pair mean value and a mean deviation limitvalue will thus serve as the baseline condition against which theindividual pressure sensor mean values are compared. As such, asubsequent calculation step will be to determine whether each individualsensor mean value is within, above or below the baseline condition. Oncethis calculation is complete, the system can then categorize each sensorinto a uniformity category that is correlated to the relationshipbetween the sensor mean value and the baseline condition. As statedbefore, median values can be used in addition to, or instead of, meanvalues.

Referring back to FIG. 10, once the categorization of the sensors and/orsensor groups has occurred, the system can then create a graphicaloutput showing at least one complete gait cycle based on the uniformitycategories. This is essentially accomplished by assigning a color ortone to each pressure uniformity category. However, it should be notedthat it is possible to arrive at the graphical output shown in FIGS. 4-8without explicitly defining a uniformity category by simply assigning acolor or tone to a mathematical comparison of the sensor data to thebaseline condition directly. In either case, the graphical output is thedirect result of a comparison between the sensor data and the chosenbaseline condition. As such, a uniformity category will exist whether ornot actually defined in the system explicitly. It is furthercontemplated that a health care professional can change any of the aboveidentified parameters to generate additional graphical outputs withoutthe need to acquire additional data. For example, the health careprofessional may want to compare the sensor data to a baseline conditionrelating to a normal gait, and also to a previous test for the sameindividual to monitor progress over time towards an ideal gait pattern.Another example would be where the health care professional wants tonarrow or broaden the values for the baseline condition to evaluate moreor less extreme patterns in the individual's gait. Furthermore, the gaitanalysis system 100 is also capable of displaying input and outputtables, such as those shown in FIGS. 12-16, which can provide a link tothe graphical output for a particular gait cycle, or for an exemplarygait cycle. FIG. 12 shows sample input data while FIG. 13 shows outputdata values for a baseline data test. An output table showing acomparison between a subsequent test and the baseline test is shown atFIGS. 14-16. It is also possible for the graphical output system toallow the health care professional to scroll the graphical output to seeall of the gait cycles that have been recorded which is particularlyuseful in analyzing the events that may have led up to a loss of balanceand fall. As such, one skilled in the art will appreciate that gaitanalysis system 100, and the graphical output it is able to provide,greatly enhances a health care professional's ability to diagnose andtreat balance and gait disorders.

Given the above description it should be appreciated that gait analysissystem 100 is able to provide quantitative data during activities ofdaily living that can be used to identify individuals who may be at riskfor gait and/or balance disorders, and the potentially injuriousconsequences of those disorders. Gait analysis system 100 is also ableto enhance a health care professional's ability to aid and monitor therehabilitation and training of individuals who either have or are atrisk for gait and/or balance disorders.

The above are example principles. Many embodiments can be made.

We claim:
 1. A method for analyzing the gait of an individual wearing a left shoe and a right shoe, the method including the steps of: (a) acquiring data from a first array comprising a plurality of shear sensors configured for placement in a left shoe and from a second array comprising a plurality of shear sensors configured for placement in a right shoe, the acquired data being separated into at least two separate gait phases for each array; (b) comparing at least a portion of the acquired data to a baseline condition for each gait phase; and (c) categorizing the sensors in each array, or a group of sensors in each array, into one of at least two uniformity categories for each gait phase based on the comparison of the acquired data to the baseline condition.
 2. The method according to claim 1, wherein the at least two separate gait phases for each array comprises a heel strike gait phase, a mid-stance gait phase, and a toe-off gait phase.
 3. The method according to claim 1, wherein the baseline condition comprises data derived from a baseline test of the individual's gait, data derived from a previous analysis of the individual's gait, pressure data derived from calculated normal gait values for the individual, or data derived from the mean value of a corresponding pair of sensors or group of sensors in each array.
 4. The method according to claim 1, wherein the step of comparing the data acquired relating to each sensor or a group of sensors in the arrays to a baseline condition includes comparing the acquired data to at least one deviation threshold value of the baseline condition.
 5. The method according to claim 1, wherein a deviation threshold value of the baseline condition is defined for each gait phase.
 6. The method according to claim 1, further comprising: (a) creating a graphical output based on the uniformity category into which each sensor or group of sensors has been placed, the output showing at least one entire gait cycle wherein each gait phase is individually represented by a right footprint and a left footprint.
 7. The method according to claim 6, wherein: (a) the graphical output shows shaded, patterned or colored areas correlating to the uniformity category for each shear sensor on each footprint for each gait phase in the gait cycle, the shaded, patterned or colored areas also being shown on each footprint at a location corresponding to the actual sensor location within the shoe.
 8. The method according to claim 7, wherein each shaded, patterned or colored area is blended or transitioned together with an adjacent shaded, patterned or colored area.
 9. The method according to claim 8, wherein the graphical output further includes showing a value for shear stress for each gait phase.
 10. The method according to claim 9, wherein the graphical output further includes showing a longitudinal component and a lateral component for the shear stress value.
 11. The method according to claim 10, wherein the shear stress and component values are shown with arrows.
 12. The method according to claim 11, wherein the graphical output further includes a torque value arrow for each gait phase.
 13. The method according to claim 12, wherein the graphical output further includes a torque value arrow for each gait phase.
 14. A method for analyzing the gait of an individual wearing a left shoe and a right shoe, the method including the steps of: (a) acquiring data from a first array comprising a plurality of pressure sensors configured for placement in a left shoe and from a second array comprising a plurality of pressure sensors configured for placement in a right shoe, the acquired data being separated into at least two separate gait phases for each array; (b) comparing at least a portion of the acquired data to a baseline condition for each gait phase; and (c) categorizing the sensors in each array, or a group of sensors in each array, into one of at least two uniformity categories for each gait phase based on the comparison of the acquired data to the baseline condition.
 15. The method according to claim 14, wherein the at least two separate gait phases for each array comprises a heel strike gait phase, a mid-stance gait phase, and a toe-off gait phase.
 16. The method according to claim 14, wherein the baseline condition comprises data derived from a baseline test of the individual's gait, data derived from a previous analysis of the individual's gait, pressure data derived from calculated normal gait values for the individual, or data derived from the mean value of a corresponding pair of sensors or group of sensors in each array.
 17. The method according to claim 14, wherein the step of comparing the data acquired relating to each sensor or a group of sensors in the arrays to a baseline condition includes comparing the acquired data to at least one deviation threshold value of the baseline condition.
 18. A method for analyzing the gait of an individual wearing a left shoe and a right shoe, the method including the steps of: a. acquiring data onto a computerized storage device: i. the acquired data comprising sensor and time information being from a first array of sensors disposed in a left shoe and a second array of sensors disposed in a right shoe; ii. each sensor in the first array having a corresponding and similarly located sensor in the second array that together form a sensor pair; b. creating a data evaluation set, including the steps of: i. parsing at least some of the acquired data into at least two separate gait phases for each array; ii. calculating a mean value for each sensor for each similar gait phase; and iii. calculating a mean value for each sensor pair for each similar gait phase; iv. analyzing the data evaluation set, including the steps of: v. comparing, for each gait phase, the mean value for each sensor to the sensor pair mean value and to a mean deviation limit value; and vi. categorizing each sensor into one of at least two uniformity categories for each gait phase on the basis of the comparison; and c. creating a graphical output based on the category into which each sensor has been placed, the output showing at least one entire gait cycle wherein each gait phase is individually represented by a right footprint and a left footprint.
 19. The method according to claim 18, wherein the first array of sensors and the second array of sensors include shear stress sensors.
 20. The method according to claim 18, wherein the first array of sensors and the second array of sensors include shear stress sensors and pressure sensors. 